How CDPs, ESPs, and data tools integrate with Snowflake
Joey Lee
December 18, 2025
For many marketing teams, Snowflake is no longer just an analytics warehouse. It has become the system of record for customer data. As customer journeys span dozens of tools and channels, marketers need a centralized, reliable foundation that connects data collection, analysis, and activation.
Customer data platforms (CDPs), email service providers (ESPs), and modern data tools increasingly use Snowflake as that foundation. Understanding how these tools integrate with Snowflake helps explain why it plays such a critical role in modern marketing stacks.
Snowflake as the customer data foundation
At a high level, Snowflake sits at the center of the marketing data ecosystem. Data flows into Snowflake from websites, mobile apps, CRMs, ad platforms, and product systems. Inside Snowflake, that data is cleaned, joined, and modeled into a unified customer view.
Once unified, that data can be activated downstream in marketing and engagement tools. This architecture ensures that analytics and activation are powered by the same source of truth.
Snowflake’s role as a centralized customer data platform foundation is increasingly common across modern marketing organizations.
ESPs and engagement platforms powered by Snowflake
Email and messaging platforms like Braze and Klaviyo increasingly integrate with Snowflake to power segmentation, personalization, and analytics.
Rather than relying only on data stored inside the ESP, marketers can sync rich customer attributes and segments from Snowflake. This allows campaigns to reflect the full customer journey, not just email interactions.
Braze and Snowflake
Braze integrates with Snowflake for both ingestion and activation. Data from Braze can be loaded into Snowflake for analysis, while curated attributes and segments can be synced back into Braze using reverse ETL tools.
Customer.io and Snowflake
Customer.io integrates with Snowflake in a way that is similar to platforms like Braze. Event, profile, and campaign data from Customer.io can be loaded into Snowflake for analysis, allowing teams to evaluate messaging performance alongside product, revenue, and behavioral data.
In addition, Customer.io supports using Snowflake as a source for customer attributes and segmentation. Teams can sync curated data from Snowflake into Customer.io, ensuring that messaging and journeys are driven by warehouse-defined logic rather than siloed ESP data.
This approach allows Snowflake to serve as the system of record for customer data, while Customer.io focuses on orchestration and delivery across email, push, and other channels.
Klaviyo and Snowflake
Klaviyo supports Snowflake as a data warehouse integration, allowing teams to analyze campaign performance alongside broader business data. Reverse ETL tools can also push Snowflake-defined segments into Klaviyo for advanced targeting.
Iterable and Snowflake
Iterable integrates with Snowflake primarily through reverse ETL workflows. Iterable’s Smart Ingest feature is powered by Hightouch, allowing teams to sync data directly from Snowflake into Iterable.
This makes it possible to define audiences, user attributes, and event logic centrally in Snowflake, then activate them in Iterable without duplicating logic inside the ESP.
CDPs and customer orchestration platforms on Snowflake
Some platforms go beyond basic engagement and position Snowflake as the core customer data layer.
Simon Data and Snowflake
Simon Data is a customer data platform designed to sit directly on top of Snowflake. Instead of copying data into a separate system, Simon Data queries and activates data directly from the warehouse.
This architecture allows marketers to build audiences, orchestrate journeys, and trigger campaigns using Snowflake as the system of record. Business logic stays centralized, and data freshness is preserved.
Bloomreach and Snowflake
Bloomreach integrates with Snowflake to support advanced personalization and commerce-focused use cases. Customer, product, and behavioral data stored in Snowflake can be used to power segmentation, recommendations, and messaging across Bloomreach’s engagement tools.
This setup allows Snowflake to remain the authoritative data layer while Bloomreach focuses on experience orchestration.
Segment and Snowflake
Segment supports Snowflake as a destination, allowing companies to store raw event data directly in the warehouse. This makes Snowflake a long-term system of record for customer behavior.
mParticle and Snowflake
mParticle offers similar functionality, with strong identity resolution and governance features. Snowflake acts as a downstream analytics and modeling layer where customer profiles are enriched and analyzed.
By integrating CDPs with Snowflake, teams gain flexibility. Instead of locking logic inside the CDP, they can model customer data centrally and reuse it across tools.
Reverse ETL as the activation layer
Reverse ETL tools are the glue between Snowflake and marketing platforms. Tools like Hightouch and Census sync data from Snowflake into CDPs, ESPs, CRMs, and ad platforms.
Instead of rebuilding logic in each tool, teams define metrics, segments, and attributes once in Snowflake. Reverse ETL then keeps downstream tools continuously updated.
Popular reverse ETL platforms include:
This approach ensures consistency across channels and reduces the risk of mismatched definitions.
Benefits of this architecture for marketers
When CDPs, ESPs, and data tools integrate with Snowflake, marketing teams gain several advantages:
A single source of truth for customer data
More accurate and flexible segmentation
Faster experimentation and iteration
Better alignment between analytics and activation
Stronger governance and data quality
This model also future-proofs marketing stacks. As tools change, Snowflake remains the stable foundation.
Why Snowflake enables marketing activation at scale
Snowflake’s architecture makes these integrations possible. Its ability to handle large volumes of data, support high concurrency, and isolate workloads ensures that analytics, transformations, and activations can run simultaneously.
Because Snowflake supports structured and semi-structured data, it can store raw event streams alongside modeled customer tables. This flexibility is essential for modern marketing use cases.
Snowflake is not replacing CDPs or ESPs. Instead, it connects them. It provides the shared data layer that allows each tool to do what it does best.
What this means for the modern marketing stack
The modern marketing stack is no longer a collection of disconnected tools. It is a coordinated system built around a central data platform.
Snowflake’s role in this system is increasingly clear. It is where customer data comes together, where business logic lives, and where activation begins.
In the next post in this series, we will explore concrete marketing use cases and show how teams use Snowflake to power reporting, personalization, and growth.
Inside Snowflake’s architecture: The magic behind the scenes
How CDPs, ESPs, and data tools integrate with Snowflake
